A constraint is a circumstance that prevents an organization from achieving higher levels of performance. Constraints typically result from limiting factors such as a shortage of skilled labor, materials, production capacity, or sales volume, and must be eliminated or reduced to improve performance. Constraints are also integral in linear programming problem statements.
A decision model is a mathematical simulation of the variables and elements inherent in business decisions, aimed at achieving the objectives of an organization by analyzing the relationships and constraints among those variables.
Goal programming is a form of linear programming that allows for consideration of multiple, potentially conflicting goals in decision-making processes.
Linear programming is a mathematical technique used for optimization, particularly suited for scenarios involving constraints. This method assists in achieving the best possible outcome, such as maximizing profits or minimizing costs, under specified limitations.
Linear Programming (LP) is a mathematical method for determining a way to achieve the best outcome in a given mathematical model for decisions with numerous alternatives. LP is often used in business, economics, and engineering to maximize profit or minimize costs in processes with varying levels of inputs.
An objective function in linear programming is a mathematical statement that defines the goal of a decision-making problem, often aiming to maximize contribution or minimize costs based on the relationship between production factors.
The shadow price represents the change in the optimal value of the objective function for a linear programming problem per unit increase in the right-hand side of a constraint.
The Simplex Method, or Simplex Algorithm, is a method used for solving linear programming problems by iteratively testing feasible solutions until reaching the optimal solution. Designed for computational efficiency, it is particularly well-suited for computer applications.
